Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We introduce new specifications and estimation procedures of traditional distance functions that allow researchers to undertake environmental efficiency and productivity studies within a parametric stochastic framework. Relying on a translog distance function specification that treats the outputs' vector asymmetrically by allowing equiproportional desirable outputs expansion and undesirable outputs contraction, we discuss the relevant properties that characterize the environmental hyperbolic distance function, and show that it can be easily implemented using conventional econometric techniques based on maximum likelihood estimation. We illustrate our model estimating technical efficiency scores for a panel of U.S. electricity generating units that produce electricity and SO2 emissions as byproduct.